Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform

被引:35
作者
Tripathy, Rajesh K. [1 ]
Zamora-Mendez, Alejandro [2 ]
Serna, Jose A. de La O. [3 ]
Arrieta Paternina, Mario R. [4 ]
Arrieta, Juan G. [5 ]
Naik, Ganesh R. N. [6 ]
机构
[1] Siksha O Anusandhan, Fac Engn & Technol ITER, Bhubaneswar, India
[2] Univ Michoacan San Nicolas Hidalgo, Elect Engn Fac, Morelia, Michoacan, Mexico
[3] Autonomous Univ Nuevo Leon, Dept Elect Engn, Monteney, Mexico
[4] Univ Nacl Autonoma Mexico, Dept Elect Engn, Mexico City, DF, Mexico
[5] Sanatorio Guemes Hosp Privado, Buenos Aires, DF, Argentina
[6] Western Sydney Univ, MARCS Inst, Biomed Engn & Neuromorph Syst BENS Res Grp, Penrith, NSW, Australia
来源
FRONTIERS IN PHYSIOLOGY | 2018年 / 9卷
关键词
life threatening arrhythmia; Taylor-Fourier transform; mangnitude and phase features; LSSVM; radial basis funtion kernel; classifier performance; EMPIRICAL MODE DECOMPOSITION; SUPPORT VECTOR MACHINE; FIBRILLATION DETECTION; TACHYCARDIA; ECG; CLASSIFICATION; SIGNALS; DEFIBRILLATORS; DIAGNOSIS; SYSTEM;
D O I
10.3389/fphys.2018.00722
中图分类号
Q4 [生理学];
学科分类号
071003 ;
摘要
Accurate detection and classification of life-threatening ventricular arrhythmia episodes such as ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) from electrocardiogram (ECG) is a challenging problem for patient monitoring and defibrillation therapy. This paper introduces a novel method for detection and classification of life-threatening ventricular arrhythmia episodes. The ECG signal is decomposed into various oscillatory modes using digital Taylor-Fourier transform (DTFT). The magnitude feature and a novel phase feature namely the phase difference (PD) are evaluated fromthe mode Taylor-Fourier coefficients of ECG signal. The least square support vector machine (LS-SVM) classifier with linear and radial basis function (RBF) kernels is employed for detection and classification of VT vs. VF, non-shock vs. shock and VF vs. non-VF arrhythmia episodes. The accuracy, sensitivity, and specificity values obtained using the proposed method are 89.81, 86.38, and 93.97%, respectively for the classification of Non-VF and VF episodes. Comparison with the performance of the state-of-the-art features demonstrate the advantages of the proposition.
引用
收藏
页数:12
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